With the world economy getting increasingly globalized, competition in markets is growing at a tremendous rate. To survive in such an environment, organizations need to evolve continuously. One of the ways for organizations to evolve is through Research & Development (R&D). R&D of new technologies and products is one of the most critical success factors in modern business. R&D helps organizations compete better by lowering costs of existing products, developing new and improved products or wholly revolutionizing a product category. Given the importance of R&D, the need to ensure that the R&D effort delivers on time becomes very critical. Hence, the organizations engaged in R&D must devise ways and means to organize and manage R&D activities.
What makes management of R&D challenging are its distinctive characteristics. R&D differs from other activities as it is based primarily on knowledge and understanding. Since different divisions of an enterprise are involved in R&D, effective transfer of knowledge is required between them. This transfer of knowledge requires a process to capture the knowledge in the first place. This capture of knowledge is hindered due to the lack of a common language across divisions that participate in R&D. Since every division talks in a different jargon, no effective means exist for transferring knowledge and understanding between divisions. Currently, most knowledge capture and transfer in R&D is ad-hoc through extensive documentation. However, it is very difficult to understand the context and rationale behind an R&D activity from these large documents. Also, often during the course of a project, some team members leave and new people join. The existing systems are unable to provide an effective way of transferring knowledge to the new people. Hence, this phenomenon often results in redundant effort and delay in the schedule.
Another factor that makes management of R&D challenging is the lack of visibility into the progress and needs of various projects. Many R&D projects are large efforts—taking multiple years and involving a large number of engineers. Such R&D projects are sub-divided into subprojects, each delivering a component of the overall product under development. It is even harder to get visibility into progress and needs of such large projects. A key reason for this is that the R&D management tools are disparate and fragmented across divisions. This fragmented information gives an incomplete and inaccurate picture of the progress. Further, even when links exist between such fragmented tools, it is very difficult to aggregate or segregate information for management purposes. Specifically, the tools to manage investments across diverse projects and technologies are undeveloped and require extensive manual labor. Further, metrics do not exist to aggregate R&D activity information to allow management to identify trends and measure performance. Very few metrics exist to analyze investment needs and determine investments across diverse R&D projects. This results in a lot of decisions, such as R&D planning and investment decisions, to be based on imprecise information or what is called “gut feelings.” This can often result in unnecessary delays in a project. In a typical organization engaged in R&D, the R&D division works on a multitude of projects simultaneously. A delay in one project has significant impact on the other projects and on the overall R&D schedule. Similarly, R&D project execution is normally distributed across multiple organizations and metrics do not exist to aggregate information and enable managers to make decisions.
In light of the above, there exists a need for an efficient R&D management system. The system should be capable of capturing knowledge across divisions by overcoming the different jargons. Further, the system should span across the divisions in an enterprise to enable effective sharing of information providing better visibility into the whole R&D process.